发表机构
School of Computing and Information Systems, Singapore Management University; Institute of Advanced Intelligence and Computing, A*STAR（新加坡管理大学计算与信息系统学院; 新加坡科研局先进智能与计算研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有用户表示方法忽略价值相关维度的问题，提出ValueGraph图预训练框架，利用自动推断的道德价值信号作为软约束，在立场检测和Twitter机器人检测任务上取得优于多种基线的性能。
AI 中文摘要
价值信号是聚合后的用户层面道德表征，用于从用户在线言论中捕捉其推断出的与价值相关的倾向。社交媒体上的用户行为不仅受用户言论内容或互动对象影响，还受其表达态度时所依托的价值信号影响。现有用户表示方法大多忽略了这一与价值相关的维度。我们提出ValueGraph，这是一个图预训练框架，它将自动推断出的道德价值信号作为情境化用户表示的噪声辅助信号。ValueGraph从“帖子-回复”图中学习语义和结构表示，并通过对比和聚类目标，基于相对价值相似度对用户进行对齐。ValueGraph不将推断出的价值视为黄金心理标签，而是将其作为表示学习的软约束。在立场检测和Twitter机器人检测任务上的实验表明，该方法在强大的基于文本、基于图以及仅使用文本的大语言模型（LLM）基线之上取得了一致的性能提升，凸显了价值信号引导作为用于社会感知用户建模的有效归纳偏置的作用。
英文摘要
Value signals are aggregated user-level moral representations that capture users' inferred value-related tendencies from their online discourse. User behavior on social media is shaped not only by what users say or whom they interact with, but also by the value signal through which they express attitudes. Existing user representation methods largely miss this value-relevant dimension. We propose ValueGraph, a graph pre-training framework that uses automatically inferred moral-value signals as noisy auxiliary signals for contextualized user representation. From post-reply graphs, ValueGraph learns semantic and structural representations and further aligns users through relative value similarity with contrastive and clustering objectives. Rather than treating inferred values as gold psychological labels, ValueGraph uses them as soft constraints for representation learning. Experiments on stance detection and twitter bot detection show consistent gains over strong text-based, graph-based, and text-only LLM baselines, highlighting value-signal guidance as a useful inductive bias for socially informed user modeling.
